Emerging Trends in Artificial Intelligence of Things With Machine Learning and Semantic Web Convergence

Abstract

This chapter explores the dynamic convergence of artificial intelligence of things (AIoT), machine learning algorithms, and the semantic web. The fusion of AI and the internet of things (IoT) creates context-aware applications with transformative potential. Machine learning enhances AIoT capabilities, empowering systems to process IoT data effectively. Simultaneously, the semantic web, with its knowledge representation frameworks, augments adaptability. Delving into deep learning, reinforcement learning, and ensemble methods, the chapter elucidates how machine learning drives autonomous decision-making in AIoT. In the semantic web, the integration of machine learning introduces dynamic knowledge adaptation. Case studies in smart environments, predictive maintenance, and recommendation systems highlight practical implementations. The chapter addresses challenges, including scalability, security, and ethical implications. Emerging trends, interdisciplinary approaches, and societal impacts are explored, emphasizing the transformative potential of AIoT and semantic web integration.